Feat: Implement NLG Engine base structure #44
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WalkthroughAdds a new abstract base class Changes
Sequence Diagram(s)sequenceDiagram
participant Caller
participant NLGEngine
participant JSON
Caller->>NLGEngine: generate_section_text(section_id, raw_data)
activate NLGEngine
NLGEngine-->>NLGEngine: validate inputs\ngenerate content (abstract)
NLGEngine->>JSON: _format_output(content)
JSON-->>NLGEngine: json_string
NLGEngine-->>Caller: json_string
deactivate NLGEngine
rect rgb(240,255,240)
Note right of NLGEngine: generate_full_report follows same flow\nbut accepts full report data
end
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes
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Pre-merge checks and finishing touches✅ Passed checks (3 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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Actionable comments posted: 0
🧹 Nitpick comments (1)
backend/app/services/nlg/nlg_engine.py (1)
41-45: Consider adding error handling for robustness.The
_format_outputhelper is straightforward, butjson.dumps()will raise aTypeErrorifcontentcontains non-serializable objects (e.g., datetime, custom objects). While this may be acceptable for a base class where implementations control the content, consider adding error handling or documentation about serialization requirements.Optional enhancement:
def _format_output(self, content: dict) -> str: """ Helper method to ensure all outputs are structured as JSON. + + Args: + content (dict): A dictionary containing only JSON-serializable types. + + Raises: + TypeError: If content contains non-serializable objects. """ return json.dumps(content)Alternatively, you could add a custom serialization handler:
def _format_output(self, content: dict) -> str: """ Helper method to ensure all outputs are structured as JSON. """ try: return json.dumps(content, default=str) except TypeError as e: raise ValueError(f"Content contains non-serializable data: {e}")
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backend/app/services/nlg/nlg_engine.py(1 hunks)
🔇 Additional comments (4)
backend/app/services/nlg/nlg_engine.py (4)
1-9: Well-structured foundation for the NLG engine.The abstract base class pattern is appropriate for defining a pluggable interface, and the docstring clearly communicates the purpose. The imports are minimal and correct.
11-24: Excellent improvements to return type and documentation.The return type annotation has been corrected to
-> str, and the docstring now explicitly states that the method returns "A JSON-formatted string" with a clear example showing the usage of_format_output. This addresses all the concerns raised in previous reviews.
26-39: Well-documented abstract method with clear contract.The return type has been corrected to
-> str, and the docstring provides a comprehensive example showing the expected JSON structure. The example clearly demonstrates how implementations should use_format_outputfor consistent JSON serialization.
1-45: Package structure is correctly configured.The NLG package is properly set up with
__init__.pyfiles at all required levels (backend/app/__init__.py,backend/app/services/__init__.py, andbackend/app/services/nlg/__init__.py). The module structure supports proper imports ofNLGEngineand related components.
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the structure for the NLG engine looks solid; merging it now! 🚀 |
This PR introduces the foundational structure for the Natural Language Generation (NLG) engine.
Changes
backend/app/services/nlg/to house NLG-related components.nlg_engine.pywhich defines the baseNLGEngineclass.generate_section_textandgenerate_full_reportmethods, preparing for future LLM provider integration.Summary by CodeRabbit
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